推荐系统（三）：基于pytorch实现DCN
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推荐系统（三）：基于pytorch实现DCN
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一、main.py
import torch
import tqdm
from sklearn.metrics import roc_auc_score
from torch.utils.data import DataLoader
import os
import numpy as np
from torchfm.dataset.criteo import CriteoDataset
from torchfm.model.dcn import DeepCrossNetworkModel
from torchfm.model.fnn import FactorizationSupportedNeuralNetworkModel
from torchfm.model.pnn import ProductNeuralNetworkModel
def get_dataset(path):
return CriteoDataset(path)
def get_model(name, dataset):
"""
Hyperparameters are empirically determined, not opitmized.
"""
field_dims = dataset.field_dims
if name == 'fnn':
return FactorizationSupportedNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
elif name == 'ipnn':
return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='inner', dropout=0.2)
elif name == 'opnn':
return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2)
elif name == 'dcn':
return DeepCrossNetworkModel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2)
else:
raise ValueError('unknown model name: ' + name)
class EarlyStopper(object):
def __init__(self, num_trials, save_path):
self.num_trials = num_trials
self.trial_counter = 0
self.best_accuracy = 0
self.best_loss = 100
self.save_path = save_path
def is_continuable(self, model, accuracy):
if accuracy > self.best_accuracy:
self.best_accuracy = accuracy
self.trial_counter = 0
torch.save(model, self.save_path)
return True
elif self.trial_counter + 1 < self.num_trials:
self.trial_counter += 1
return True
else:
return False
def is_continue_loss(self, model, loss):
if loss < self.best_loss:
self.best_loss = loss
self.trial_counter = 0
torch.save(model, self.save_path)
return True
elif self.trial_counter + 1 < self.num_trials:
self.trial_counter += 1
return True
else:
return False
def train(model, optimizer, data_loader, criterion, device, log_interval=100):
model.train()
total_loss = 0
tk0 = tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0)
for i, (fields, target) in enumerate(tk0):
fields, target = fields.to(device), target.to(device)
y = model(fields)
loss = criterion(y, target.float())
model.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
if (i + 1) % log_interval == 0:
tk0.set_postfix(loss=total_loss / log_interval)
total_loss = 0
def my_test(model, data_loader, device, criterion):
model.eval()
targets, predicts = list(), list()
with torch.no_grad():
for fields, target in tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0):
fields, target = fields.to(device), target.to(device)
y = model(fields)
targets.extend(target.tolist())
predicts.extend(y.tolist())
loss = criterion(y, target.float())
return loss, roc_auc_score(targets, predicts)
def main(dataset_path,
model_name,
epoch,
learning_rate,
batch_size,
weight_decay,
device,
save_dir):
device = torch.device(device)
dataset = get_dataset(dataset_path)
dims = dataset.field_dims
npy_out = "field_dims.npy"
if not os.path.exists(npy_out):
np.save(npy_out, dims)
train_length = int(len(dataset) * 0.8)
valid_length = int(len(dataset) * 0.1)
test_length = len(dataset) - train_length - valid_length
train_dataset, valid_dataset, test_dataset = torch.utils.data.random_split(
dataset, (train_length, valid_length, test_length))
train_data_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=0)
valid_data_loader = DataLoader(valid_dataset, batch_size=batch_size, num_workers=0)
test_data_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=0)
model = get_model(model_name, dataset).to(device)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate, weight_decay=weight_decay)
early_stopper = EarlyStopper(num_trials=5, save_path=f'{save_dir}/{model_name}.pt')
for epoch_i in range(epoch):
train(model, optimizer, train_data_loader, criterion, device)
loss, auc = my_test(model, valid_data_loader, device, criterion)
print('epoch:', epoch_i, 'validation: auc:', auc, "loss:", loss)
if not early_stopper.is_continuable(model, auc):
print(f'validation: best auc: {early_stopper.best_accuracy}')
break
loss, auc = my_test(model, test_data_loader, device, criterion)
print(f'test loss: {loss}, test auc: {auc}')
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', default=r'E:ecommend_systemdata rain.txt')
parser.add_argument('--model_name', default='dcn')
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--learning_rate', type=float, default=0.005)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--weight_decay', type=float, default=1e-6)
parser.add_argument('--device', default='cpu')
parser.add_argument('--save_dir', default='E:/recommend_system/chkpt')
args = parser.parse_args()
main(args.dataset_path,
args.model_name,
args.epoch,
args.learning_rate,
args.batch_size,
args.weight_decay,
args.device,
args.save_dir)
二、predicts.py
import numpy as np
from functools import lru_cache
import math
import torch
@lru_cache(maxsize=None)
def convert_numeric_feature(val: str):
if val == '':
return 'NULL'
v = int(val)
if v > 2:
return str(int(math.log(v) ** 2))
else:
return str(v - 2)
class InferencePredict():
def __init__(self):
mapper_path = "mapper.npy"
self.model_path = r"E:ecommend_systemchkptdcn.pt"
"""數(shù)值和類別總列數(shù)"""
self.NUM_FEATS = 23
"""數(shù)值類別"""
self.NUM_INT_FEATS = 7
read_dictionary = np.load(mapper_path, allow_pickle=True).item()
self.feat_mapper = read_dictionary["feat_mapper"]
self.defaults = read_dictionary["defaults"]
self.model = torch.load(self.model_path)
self.model.eval()
def get_data(self, dataline, feat_mapper,defaults):
values = dataline.rstrip('
').split(' ')
np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32)
np_array[0] = int(values[0])
for i in range(1, self.NUM_INT_FEATS + 1):
np_array[i] = feat_mapper[i].get(convert_numeric_feature(values[i]), defaults[i])
for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1):
np_array[i] = feat_mapper[i].get(values[i], defaults[i])
return np_array
def predict(self, dataline):
np_array = self.get_data(dataline, self.feat_mapper,self.defaults).astype(dtype=np.long)
x,y = np_array[1:], np_array[0]
with torch.no_grad():
output = self.model(torch.from_numpy(x))
print(output)
preds = int(torch.round(output).item())
return preds, y
def main(self):
s1 = "1 450 18 47 619 153 12 0 male daily 1 0 0 0 0 MD280612 BR803759 PV285968 CT470265 PF470265 10 0 2 0 W441,W19887,W45818,W63,W894,W38883,W135945,W1684,W72349,W15298,W858,W38883"
s2 = "0 290 0 34 0 0 12 5 male monthly 1 0 0 0 0 MD441850 BR803759 PV419710 CT378940 PF470265 0 0 3 0 W605,W396,W554,W6275,W6257,W651,W51,W32265,W682,W250,W97,W1748"
s3 = "0 290 0 34 0 0 12 5 male monthly 1 0 0 0 0 MD441850 BR803759 PV419710 CT378940 PF470265 0 0 3 0 W605,W396,W554,W6275,W6257,W651,W51,W32265,W682,W250,W97,W1748"
self.predict(s1)
self.predict(s2)
self.predict(s3)
if __name__ == '__main__':
InferencePredict().main()
三、critio.py
import math
import shutil
import struct
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
import os
class CriteoDataset(torch.utils.data.Dataset):
"""
Criteo Display Advertising Challenge Dataset
Data prepration:
* Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature
* Discretize numerical values by log2 transformation which is proposed by the winner of Criteo Competition
:param dataset_path: criteo train.txt path.
:param cache_path: lmdb cache path.
:param rebuild_cache: If True, lmdb cache is refreshed.
:param min_threshold: infrequent feature threshold.
Reference:
https://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset
https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf
"""
def __init__(self, dataset_path=None, cache_path='.criteo', rebuild_cache=True, min_threshold=10):
"""數(shù)值和類別總列數(shù)"""
self.NUM_FEATS = 23
"""數(shù)值類別"""
self.NUM_INT_FEATS = 7
self.min_threshold = min_threshold
if rebuild_cache or not Path(cache_path).exists():
shutil.rmtree(cache_path, ignore_errors=True)
if dataset_path is None:
raise ValueError('create cache: failed: dataset_path is None')
self.__build_cache(dataset_path, cache_path)
self.env = lmdb.open(cache_path, create=False, lock=False, readonly=True)
with self.env.begin(write=False) as txn:
self.length = txn.stat()['entries'] - 1
self.field_dims = np.frombuffer(txn.get(b'field_dims'), dtype=np.uint32)
def __getitem__(self, index):
with self.env.begin(write=False) as txn:
np_array = np.frombuffer(
txn.get(struct.pack('>I', index)), dtype=np.uint32).astype(dtype=np.long)
return np_array[1:], np_array[0]
def __len__(self):
return self.length
def __build_cache(self, path, cache_path):
feat_mapper, defaults = self.__get_feat_mapper(path)
my_dict = {"feat_mapper":feat_mapper, "defaults": defaults}
my_path = "mapper.npy"
if not os.path.exists(my_path):
np.save(my_path, my_dict)
with lmdb.open(cache_path, map_size=int(1e11)) as env:
field_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32)
for i, fm in feat_mapper.items():
field_dims[i - 1] = len(fm) + 1
with env.begin(write=True) as txn:
txn.put(b'field_dims', field_dims.tobytes())
for buffer in self.__yield_buffer(path, feat_mapper, defaults):
with env.begin(write=True) as txn:
for key, value in buffer:
txn.put(key, value)
def __get_feat_mapper(self, path):
feat_cnts = defaultdict(lambda: defaultdict(int))
with open(path) as f:
pbar = tqdm(f, mininterval=1, smoothing=0.1)
pbar.set_description('Create criteo dataset cache: counting features')
for line in pbar:
values = line.rstrip('
').split(' ')
for i in range(1, self.NUM_INT_FEATS + 1):
feat_cnts[i][convert_numeric_feature(values[i])] += 1
for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1):
feat_cnts[i][values[i]] += 1
feat_mapper = {i: {feat for feat, c in cnt.items() if c >= self.min_threshold} for i, cnt in feat_cnts.items()}
feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in feat_mapper.items()}
defaults = {i: len(cnt) for i, cnt in feat_mapper.items()}
return feat_mapper, defaults
def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)):
item_idx = 0
buffer = list()
with open(path) as f:
pbar = tqdm(f, mininterval=1, smoothing=0.1)
pbar.set_description('Create criteo dataset cache: setup lmdb')
for line in pbar:
values = line.rstrip('
').split(' ')
np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32)
np_array[0] = int(values[0])
for i in range(1, self.NUM_INT_FEATS + 1):
np_array[i] = feat_mapper[i].get(convert_numeric_feature(values[i]), defaults[i])
for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1):
np_array[i] = feat_mapper[i].get(values[i], defaults[i])
buffer.append((struct.pack('>I', item_idx), np_array.tobytes()))
item_idx += 1
if item_idx % buffer_size == 0:
yield buffer
buffer.clear()
yield buffer
@lru_cache(maxsize=None)
def convert_numeric_feature(val: str):
if val == '':
return 'NULL'
v = int(val)
if v > 2:
return str(int(math.log(v) ** 2))
else:
return str(v - 2)
四、DCN.py
import torch
from torchfm.layer import FeaturesEmbedding, CrossNetwork, MultiLayerPerceptron
class DeepCrossNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Deep & Cross Network.
Reference:
R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.
"""
def __init__(self, field_dims, embed_dim, num_layers, mlp_dims, dropout):
super().__init__()
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.embed_output_dim = len(field_dims) * embed_dim
self.cn = CrossNetwork(self.embed_output_dim, num_layers)
self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout, output_layer=False)
self.linear = torch.nn.Linear(mlp_dims[-1] + self.embed_output_dim, 1)
def forward(self, x):
"""
:param x: Long tensor of size ``(batch_size, num_fields)``
"""
embed_x = self.embedding(x).view(-1, self.embed_output_dim)
x_l1 = self.cn(embed_x)
h_l2 = self.mlp(embed_x)
x_stack = torch.cat([x_l1, h_l2], dim=1)
p = self.linear(x_stack)
return torch.sigmoid(p.squeeze(1))
五、layer.py
import numpy as np
import torch
import torch.nn.functional as F
class FeaturesLinear(torch.nn.Module):
def __init__(self, field_dims, output_dim=1):
super().__init__()
self.fc = torch.nn.Embedding(sum(field_dims), output_dim)
self.bias = torch.nn.Parameter(torch.zeros((output_dim,)))
self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long)
def forward(self, x):
"""
:param x: Long tensor of size ``(batch_size, num_fields)``
"""
x = x + x.new_tensor(self.offsets).unsqueeze(0)
return torch.sum(self.fc(x), dim=1) + self.bias
class FeaturesEmbedding(torch.nn.Module):
def __init__(self, field_dims, embed_dim):
super().__init__()
self.embedding = torch.nn.Embedding(sum(field_dims), embed_dim)
self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long)
torch.nn.init.xavier_uniform_(self.embedding.weight.data)
def forward(self, x):
"""
:param x: Long tensor of size ``(batch_size, num_fields)``
"""
x = x + x.new_tensor(self.offsets).unsqueeze(0)
return self.embedding(x)
class FieldAwareFactorizationMachine(torch.nn.Module):
def __init__(self, field_dims, embed_dim):
super().__init__()
self.num_fields = len(field_dims)
self.embeddings = torch.nn.ModuleList([
torch.nn.Embedding(sum(field_dims), embed_dim) for _ in range(self.num_fields)
])
self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long)
for embedding in self.embeddings:
torch.nn.init.xavier_uniform_(embedding.weight.data)
def forward(self, x):
"""
:param x: Long tensor of size ``(batch_size, num_fields)``
"""
x = x + x.new_tensor(self.offsets).unsqueeze(0)
xs = [self.embeddings[i](x) for i in range(self.num_fields)]
ix = list()
for i in range(self.num_fields - 1):
for j in range(i + 1, self.num_fields):
ix.append(xs[j][:, i] * xs[i][:, j])
ix = torch.stack(ix, dim=1)
return ix
class FactorizationMachine(torch.nn.Module):
def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
square_of_sum = torch.sum(x, dim=1) ** 2
sum_of_square = torch.sum(x ** 2, dim=1)
ix = square_of_sum - sum_of_square
if self.reduce_sum:
ix = torch.sum(ix, dim=1, keepdim=True)
return 0.5 * ix
class MultiLayerPerceptron(torch.nn.Module):
def __init__(self, input_dim, embed_dims, dropout, output_layer=True):
super().__init__()
layers = list()
for embed_dim in embed_dims:
layers.append(torch.nn.Linear(input_dim, embed_dim))
layers.append(torch.nn.BatchNorm1d(embed_dim))
layers.append(torch.nn.ReLU())
layers.append(torch.nn.Dropout(p=dropout))
input_dim = embed_dim
if output_layer:
layers.append(torch.nn.Linear(input_dim, 1))
self.mlp = torch.nn.Sequential(*layers)
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, embed_dim)``
"""
return self.mlp(x)
class InnerProductNetwork(torch.nn.Module):
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
num_fields = x.shape[1]
row, col = list(), list()
for i in range(num_fields - 1):
for j in range(i + 1, num_fields):
row.append(i), col.append(j)
return torch.sum(x[:, row] * x[:, col], dim=2)
class OuterProductNetwork(torch.nn.Module):
def __init__(self, num_fields, embed_dim, kernel_type='mat'):
super().__init__()
num_ix = num_fields * (num_fields - 1) // 2
if kernel_type == 'mat':
kernel_shape = embed_dim, num_ix, embed_dim
elif kernel_type == 'vec':
kernel_shape = num_ix, embed_dim
elif kernel_type == 'num':
kernel_shape = num_ix, 1
else:
raise ValueError('unknown kernel type: ' + kernel_type)
self.kernel_type = kernel_type
self.kernel = torch.nn.Parameter(torch.zeros(kernel_shape))
torch.nn.init.xavier_uniform_(self.kernel.data)
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
num_fields = x.shape[1]
row, col = list(), list()
for i in range(num_fields - 1):
for j in range(i + 1, num_fields):
row.append(i), col.append(j)
p, q = x[:, row], x[:, col]
if self.kernel_type == 'mat':
kp = torch.sum(p.unsqueeze(1) * self.kernel, dim=-1).permute(0, 2, 1)
return torch.sum(kp * q, -1)
else:
return torch.sum(p * q * self.kernel.unsqueeze(0), -1)
class CrossNetwork(torch.nn.Module):
def __init__(self, input_dim, num_layers):
super().__init__()
self.num_layers = num_layers
self.w = torch.nn.ModuleList([
torch.nn.Linear(input_dim, 1, bias=False) for _ in range(num_layers)
])
self.b = torch.nn.ParameterList([
torch.nn.Parameter(torch.zeros((input_dim,))) for _ in range(num_layers)
])
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
x0 = x
for i in range(self.num_layers):
xw = self.w[i](x)
x = x0 * xw + self.b[i] + x
return x
class AttentionalFactorizationMachine(torch.nn.Module):
def __init__(self, embed_dim, attn_size, dropouts):
super().__init__()
self.attention = torch.nn.Linear(embed_dim, attn_size)
self.projection = torch.nn.Linear(attn_size, 1)
self.fc = torch.nn.Linear(embed_dim, 1)
self.dropouts = dropouts
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
num_fields = x.shape[1]
row, col = list(), list()
for i in range(num_fields - 1):
for j in range(i + 1, num_fields):
row.append(i), col.append(j)
p, q = x[:, row], x[:, col]
inner_product = p * q
attn_scores = F.relu(self.attention(inner_product))
attn_scores = F.softmax(self.projection(attn_scores), dim=1)
attn_scores = F.dropout(attn_scores, p=self.dropouts[0], training=self.training)
attn_output = torch.sum(attn_scores * inner_product, dim=1)
attn_output = F.dropout(attn_output, p=self.dropouts[1], training=self.training)
return self.fc(attn_output)
class CompressedInteractionNetwork(torch.nn.Module):
def __init__(self, input_dim, cross_layer_sizes, split_half=True):
super().__init__()
self.num_layers = len(cross_layer_sizes)
self.split_half = split_half
self.conv_layers = torch.nn.ModuleList()
prev_dim, fc_input_dim = input_dim, 0
for i in range(self.num_layers):
cross_layer_size = cross_layer_sizes[i]
self.conv_layers.append(torch.nn.Conv1d(input_dim * prev_dim, cross_layer_size, 1,
stride=1, dilation=1, bias=True))
if self.split_half and i != self.num_layers - 1:
cross_layer_size //= 2
prev_dim = cross_layer_size
fc_input_dim += prev_dim
self.fc = torch.nn.Linear(fc_input_dim, 1)
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
xs = list()
x0, h = x.unsqueeze(2), x
for i in range(self.num_layers):
x = x0 * h.unsqueeze(1)
batch_size, f0_dim, fin_dim, embed_dim = x.shape
x = x.view(batch_size, f0_dim * fin_dim, embed_dim)
x = F.relu(self.conv_layers[i](x))
if self.split_half and i != self.num_layers - 1:
x, h = torch.split(x, x.shape[1] // 2, dim=1)
else:
h = x
xs.append(x)
return self.fc(torch.sum(torch.cat(xs, dim=1), 2))
class AnovaKernel(torch.nn.Module):
def __init__(self, order, reduce_sum=True):
super().__init__()
self.order = order
self.reduce_sum = reduce_sum
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
batch_size, num_fields, embed_dim = x.shape
a_prev = torch.ones((batch_size, num_fields + 1, embed_dim), dtype=torch.float).to(x.device)
for t in range(self.order):
a = torch.zeros((batch_size, num_fields + 1, embed_dim), dtype=torch.float).to(x.device)
a[:, t+1:, :] += x[:, t:, :] * a_prev[:, t:-1, :]
a = torch.cumsum(a, dim=1)
a_prev = a
if self.reduce_sum:
return torch.sum(a[:, -1, :], dim=-1, keepdim=True)
else:
return a[:, -1, :]
總結(jié)
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